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This page holds a few example code-snippets for use in pyana analysis. The analysis is written in python and uses MatPlotLib.PyPlot for plotting of data. Compare with myana user examples to see how (some of) the same things can be done using the myana analysis framework.

Time data

The time of the event can be obtained within the event function:

...


def event ( self, evt, env ) :
    event_time = evt.getTime().seconds() + 1.0e-9*evt.getTime().nanoseconds() )

The most reliable place for up-to-date information about all the event getters in pyana, see: https://confluence.slac.stanford.edu/display/PCDS/Pyana+Reference+Manual#PyanaReferenceManual-Classpyana.event.Event

For all the examples, you may assume that the pyana module contains a class with at least 'beginjob', 'event' and 'endjob' functions that starts something like this

IPIMB diode data

Currently there are two data structures that holds data from the same type of devices. Depending on DAQ
configuration, they are either DetInfo type or BldInfo type. Here are examples for extracting both types
in the user module event function:

Code Block

	none
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title	outline of a pyana module


import numpy as np
import matplotlib.pyplot as plt
from pypdsdata import xtc

class mypyana(object):
    def __init__(self,source="")
def event(self, evt, env):
    # raw data
    ipmRaw = evt.get(xtc.TypeId.Type.Id_IpimbData, source )
    try:
        chself.source = [ipmRaw.channel0(),source
        self.counter = None
    ipmRaw.channel1(),
      self.array = []   # really just a list

    def ipmRaw.channel2(),beginjob(self,evt,env):
        self.counter = 0

    def ipmRaw.channel3() ]event(self,evt,env):
        self.counter += 1

      
  # snippet code goes here
  ch_volt      thedata = [ipmRawevt.channel0Volts(),
  get(xtc.TypeId.Type.Id_SomeType, self.source )
        self.array.append( thedata.somevalue )

    def   ipmRaw.channel1Volts(),endjob(self,evt,env):
       print "Job done! Processed %d events. " % self.counter

    ipmRaw.channel2Volts(),
        # place for plotting etc

       # convert from python list to a ipmRaw.channel3Volts() ]numpy array
    except:
   self.array = np.array( self.array  pass)

       # feature-extractedplot datagraph
     ipmFex = evtplt.getplot(xtc.TypeId.Type.Id_IpmFex, source )
    try:
  self.array)

BeamLine Data: EBeam

To read out energy, charge and position of the beam from the beamline data, use getEBeam(). It returns a class/structure that has the following members/fields:

Code Block

	none
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title	getEBeam


def event(self,evt,env):

    ebeam = evt.getEBeam()
 # array of 4try numbers:
         fex_channelbeamChrg = ipmFexebeam.channel 
fEbeamCharge
        beamEnrg # scalar values= ebeam.fEbeamL3Energy
        beamPosX fex_sum = ipmFexebeam.sum fEbeamLTUPosX
        beamPosY fex_xpos = ipmFexebeam.xposfEbeamLTUPosY
         fex_yposbeamAngX = ipmFexebeam.yposfEbeamLTUAngX

     except:
   beamAngY = ebeam.fEbeamLTUAngY
    pass

Code Block

none


def event(self, evt, env):
   beamPkCr ipm = evt.getSharedIpimbValue("HFX-DG3-IMB-02")ebeam.fEbeamPkCurrBC2
    # or equivalently:
  print "ebeam: #", ipmbeamChrg, = evt.get(xtc.TypeId.Type.Id_SharedIpimb, "HFX-DG3-IMB-02")
    try: beamEnrg, beamPosX, beamPosY, beamAngX, beamAngY, beamPkCr
        ### Raw data ###except:
        #print arrays"No ofEBeam 4 numbers:
    object found"

BeamLine Data: FEE Gas Detector

To read out the energy from the front end enclosure (FEE) gas detector, use getFeeGasDet(). This returns and array of 4 numbers:

Code Block

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title	getFeeGasDet


    chfee_energy_array = [ipm.ipimbData.channel0evt.getFeeGasDet(),
    gdENRC11 = fee_energy_array[0]
    gdENRC12 =   ipm.ipimbData.channel1(),fee_energy_array[1]
    gdENRC21 = fee_energy_array[2]
    gdENRC22 = fee_energy_array[3]

    energy = (gdENRC21  ipm.ipimbData.channel2(),
  + gdENRC22) / 2.0
    # or use the first two that  ipm.ipimbData.channel3()]
  has a different gain:
      ch_voltenergy = (gdENRC11 + gdENRC12) / 2.0

BeamLine Data: Phase Cavity

To read out fit time and charge of the phase cavity, use getPhaseCavity() which returns a structure with the following fields:

Code Block

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title	getPhaseCavity


[ipm.ipimbData.channel0Volts(),
                   ipm.ipimbData.channel1Volts(),
            pc       ipm.ipimbData.channel2Volts= evt.getPhaseCavity(),
     try:
         pcFitTime1 =    ipm.ipimbData.channel3Volts()]
pc.fFitTime1
        ### Feature-extractedpcFitTime2 data= ###pc.fFitTime2
        # arraypcCharge1 of 4 numbers:
= pc.fCharge1
         fex_channelspcCharge2 = ipm.ipmFexData.channel pc.fCharge2
        
 print "PhaseCavity: ", pcFitTime1,  pcFitTime2,  # scalars:pcCharge1, pcCharge2
      except :
 fex_sum  = ipm.ipmFexData.sum 
    print "No Phase Cavity fex_xpos = ipm.ipmFexData.xpos
   object found"

Event code

Code Block

	none
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title	EnvData


def event(self, evt, env):
    evrdata fex_ypos = ipm.ipmFexData.ypos

 evt.getEvrData("NoDetector-0|Evr-0")
    except:
    for i in   pass

Acqiris waveform data

This method can be used for any detector/device that has Acqiris waveform data. Edit the self.address field to get the detector of your choice.

Initialize class members:

Code Block
def __init__ ( self range (evrdata.numFifoEvents()): #print initialize data self.address = "AmoITof-0\|Acqiris-0" self.data = [] self.counter = 0

If you're curious to see any of the Acqiris configuration, e.g. how many channels do we have, you can inspect the AcqConfig object:

"Event code: ", evrdata.fifoEvent(i).EventCode

In the example above, the address of the EvrData object is given as "NoDetector-0|Evr-0". The address may be different in other cases, so make sure you have the correct address. If you don't know what it is, you can use 'pyxtcreader -vv <xtcfile> | less' to browse your xtcfile and look for it. Look for a lines with 'contains=EvrConfig_V' or 'contains=EvrData_V'. The address will be found on the same line in 'src=DetInfo(<address>)'

Encoder data (delay scanner)

Code Block

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title	EncoderData


def event(self,evt,env)

Code Block


    def beginjob ( self, evt, env ) :
    try:
    cfg    encoder = envevt.getConfigget( _pdsdata.xtc.TypeId.Type.Id_AcqConfigEncoderData, self.addressenc_source )
        self.numencoder_value = cfgencoder.nbrChannelsvalue()

The read the event waveform data (an array) and append it to the self.data list:

Code Block
except: def event ( self, evt,print env ) : "No encoder found in this event" channel = 0 return

You could combine it with phase cavity time, and compute a time delay from it, for example (I don't know the origin of these parameters!):

Code Block

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    # Encoder Parameters to acqDataconvert = evt.getAcqValue( self.address, channel, env)
   to picoseconds
    delay_a = -80.0e-6;
    delay_b if acqData := 0.52168;
    delay_c = 299792458;
    delay_0  self.counter+=1= 0;

    delay_time = (delay_a * encoder_value    wf = acqData.waveform()+ delay_b)*1.e-3 / delay_c) 
   # returnsdelay_time a= waveform2 array* of numpy.ndarray type.
            self.data.append(wf)

At the end of the job, take the average and plot it:

delay_time / 1.0e-12 + delay_0 + pcFitTime1

Time data

The time of the event can be obtained within the event function:

Code Block

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title	getTime


def event

Code Block


    def endjob( self, evt, env ) :

        data event_time = npevt.arraygetTime(self).dataseconds()  # this is an array of shape (Nevents, nSamples)

        # take the mean of all events for each sampling time
        xs = np.mean(data, axis=0)

        plt.plot(xs)

        plt.xlabel('Seconds')
        plt.ylabel('Volts')+ 1.0e-9*evt.getTime().nanoseconds() )

IPIMB diode data

This is data from sets of 4 diodes around the beam line (Intensity Position, Intensity Monitoring Boards)
that measures the beam intensity in four spots, from which we can also deduce the position of the beam.

Currently there are two data structures that holds data from the same type of devices. Depending on DAQ
configuration, they are either DetInfo type or BldInfo type. Here are examples for extracting both types
in the user module event function:

Code Block

	none
	none
title	DetInfo


def event(self, evt, env):
    # raw data
    ipmRaw = evt.get(xtc.TypeId.Type.Id_IpimbData, source )
    try:
        ch = [ipmRaw.channel0(),
        plt.show      ipmRaw.channel1()

Which gives you a plot like this
Image Removed

Beamline data (Bld)

To read out energy, charge and position of the beam from the beamline data, use getEBeam(). It returns a class/structure that has the following members/fields:

Code Block


,
              ipmRaw.channel2(),
            ebeam = evtipmRaw.getEBeamchannel3() ]
          if ebeam :
    
        beamChrgch_volt = ebeam.fEbeamCharge[ipmRaw.channel0Volts(),
            beamEnrg = ebeam.fEbeamL3Energy
      ipmRaw.channel1Volts(),
      beamPosX = ebeam.fEbeamLTUPosX
            beamPosY = ebeam.fEbeamLTUPosY
    ipmRaw.channel2Volts(),
        beamAngX = ebeam.fEbeamLTUAngX
            beamAngY = ebeam.fEbeamLTUAngYipmRaw.channel3Volts()]
    except:
        beamPkCr = ebeam.fEbeamPkCurrBC2pass

    # feature-extracted data
    ipmFex  print "ebeam: ", beamChrg, beamEnrg, beamPosX, beamPosY, beamAngX, beamAngY, beamPkCr= evt.get(xtc.TypeId.Type.Id_IpmFex, source )
    try:
         # array of 4 numbers
        else : fex_channel = ipmFex.channel 

         # scalar values
 print "No EBeam object found"

To read out the energy from the front end enclosure (FEE) gas detector, use getFeeGasDet(). This returns and array of 4 numbers:

Code Block


       fee_energy_arrayfex_sum = evt.getFeeGasDet()ipmFex.sum 
        gdENRC11 fex_xpos = fee_energy_array[0]ipmFex.xpos
        gdENRC12 = fee_energy_array[1]fex_ypos = ipmFex.ypos

     except:
   gdENRC21 = fee_energy_array[2]
    pass

Code Block

	none
	none
title	BldInfo


def event(self,   gdENRC22 = fee_energy_array[3]evt, env):
    ipm    print "GasDet energy ", gdENRC11, gdENRC12, gdENRC21, gdENRC22

To read out fit time and charge of the phase cavity, use getPhaseCavity() which returns a structure with the following fields:

Code Block

= evt.getSharedIpimbValue("HFX-DG3-IMB-02")
    # or equivalently:
    # ipm   pc = evt.getPhaseCavity()get(xtc.TypeId.Type.Id_SharedIpimb, "HFX-DG3-IMB-02")
    try: 
   if pc :
   ### Raw data ###
      pcFitTime1 = pc.fFitTime1
# arrays of 4 numbers:
        pcFitTime2ch = pc.fFitTime2[ipm.ipimbData.channel0(),
            pcCharge1 = pc.fCharge1ipm.ipimbData.channel1(),
            pcCharge2 = pc.fCharge2
ipm.ipimbData.channel2(),
              ipm.ipimbData.channel3()]
     print "PhaseCavity: ", pcFitTime1,  pcFitTime2, pcCharge1, pcCharge2
 ch_volt = [ipm.ipimbData.channel0Volts(),
          else :
        ipm.ipimbData.channel1Volts(),
     print "No Phase Cavity object found"

Display images from princeton camera

When plotting with MatPlotLib, we don't need to set the limits of the histogram manually, thus we don't need to read the Princeton configuration for this. If we want to sum the image from several events, we must first define and initialize some variables:

Code Block
def __init__ ( self ): ipm.ipimbData.channel2Volts(), # initialize data self.address = "SxrEndstation-0\|Princeton-0" ipm.ipimbData.channel3Volts()] ### Feature-extracted self.data = None

In each event, we add the image array returned from the getPrincetonValue function:

Code Block


  def event ( self, evt, env ) :

###
        # array of 4 numbers:
        framefex_channels = evt.getPrincetonValue( self.address, env)
 ipm.ipmFexData.channel 
      if frame :
           # accumulate the datascalars:
        fex_sum   if self.data is None := ipm.ipmFexData.sum 
        fex_xpos       self.data = np.float_(frame.data())= ipm.ipmFexData.xpos
        fex_ypos   else := ipm.ipmFexData.ypos

     except:
          self.data += frame.data()

At the end of the job, display/save the array:

pass

Acqiris waveform data

This method can be used for any detector/device that has Acqiris waveform data. Edit the self.address field to get the detector of your choice.

Initialize class members:

Code Block

	none
	none

Code Block

    def __init__ endjob( self, env ) :
        # plt.imshow( self.data/self.countpass, origin='lower')
        plt.colorbar()initialize data
        self.address =  "AmoITof-0|Acqiris-0"
        plt.show()

  self.data = []
      # save theself.counter full image to a png file
        plt.imsave(fname="pyana_princ_image.png",arr=self.data, origin='lower')

Note that imsave saves the image only, pixel by pixel. If you want a view of the figure itself, lower resolution, you can save it from the interactive window you get from plt.show().
Image Removed

CsPad data

= 0

If you're curious to see any of the Acqiris configuration, e.g. how many channels do we have, you can inspect the AcqConfig object:

Code Block

	none
	none


    def beginjob ( self, evt, env ) :
        cfg = env.getConfig( _pdsdata.xtc.TypeId.Type.Id_AcqConfig, self.address )
        self.num = cfg.nbrChannels()

The read the event waveform data (an array) and append it to the self.data listHere's an example of getting CsPad data from an event:

Code Block

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    def event ( self, evt, env ) :
    quads    channel = evt.getCsPadQuads(self.img_source, env)
0
      if not quadsacqData :
    = evt.getAcqValue( self.address, channel, env)
    print '*** cspad information isif missing ***'acqData :
        return
    self.counter+=1
    
    # dump information about quadrants
wf = acqData.waveform()  print "Number of quadrants: %d" % len(quads)# returns a waveform array of numpy.ndarray type.
    
     for q in quads: self.data.append(wf)

At the end of the job, take the average and plot it:

Code Block

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    def endjob( self, env print) ":

  Quadrant %d" % q.quad()
        print "    virtual_channel: %s" % q.virtual_channel()
        print "    lane: %s" % q.lane()
        print "    tid: %s" % q.tid()
      data = np.array(self.data)  # this is an array of shape (Nevents, nSamples)

        # take the mean of all events for each sampling time
        xs = np.mean(data, axis=0)

        plt.plot(xs)

        print "plt.xlabel('Seconds')
     acq_count: %s" % qplt.acq_count(ylabel('Volts')
        print "    op_code: %s" % q.op_code()
        print "    seq_count: %s" % q.seq_count()plt.show()

Which gives you a plot like this
Image Added

Princeton camera image

When plotting with MatPlotLib, we don't need to set the limits of the histogram manually, thus we don't need to read the Princeton configuration for this. If we want to sum the image from several events, we must first define and initialize some variables:

Code Block

	none
	none


   def __init__ ( self ):
        # initialize data
        printself.address =  "SxrEndstation-0|Princeton-0"
     ticks: %s" % self.data = None

In each event, we add the image array returned from the getPrincetonValue function:

Code Block

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title	getPrincetonValue


  def event ( self, evt, env ) :
q.ticks()
        print "    fiducials: %s" % q.fiducials()
       frame print "= evt.getPrincetonValue( self.address, env)
     frame_type: %s" %if q.frame_type()frame :
        print "  # accumulate sb_temp: %s" % map(q.sb_temp, range(4))
the data
           if self.data is None :
         # image data as 3-dimentional array   self.data = np.float_(frame.data())
           else :
               self.data += qframe.data()

data2 will give you the third section stored, but be aware that sections sometimes are missing,
and in this case you'll need to check with the configuration information that you can obtain in beginjob:

At the end of the job, display/save the array:

Code Block


   def endjob( self, env )

Code Block

none


def beginjob(self,evt,env):
    config   = envplt.getConfigimshow(xtc.TypeId.Type.Id_CspadConfig, self.img_source self.data/self.countpass, origin='lower')
    if not config:
  plt.colorbar()
      print '*** cspad config object is missing ***' plt.show()

        return
# save the full image to a png file
    quads =   plt.imsave(fname="pyana_princ_image.png",arr=self.data, origin='lower')

Note that imsave saves the image only, pixel by pixel. If you want a view of the figure itself, lower resolution, you can save it from the interactive window you get from plt.show().
Image Added

PnCCD image

Code Block

	none
	none
title	getPnCcdValue


def event(self,evt,env):
    try:
        frame = evt.getPnCcdValue( self.source, env )
        image = frame.data()
    except:
        pass

Other image (FCCD*,Opal,PIM (TM6740), ... )

These all use the generic getFrameValue function:

Code Block

	none
	none
title	getFrameValue


def event(self,evt,env):
    try:
        frame = evt.getFrameValue( self.source )
        image = frame.data()
    except:
        pass

FCCD (Fast CCD) image

The Fast CCD is read out as two 8-bit images, therefore you need this extra line to convert it to the right format.

Code Block

	none
	none
title	getFrameValue


def event(self,evt,env):
    try:
        frame = evt.getFrameValue( self.source )
        image = frame.data()
    except:
        pass

    # convert to 16-bit integer
    image.dtype = np.uint16

CsPad data

Here's an example of getting CsPad data from an event:

Code Block

	none
	none
title	getCsPadQuads


def event(self,evt,env):
    quads = evt.getCsPadQuads(self.img_source, env)
    if not quads :
        print '*** cspad information is missing ***'
        return
        
    # dump information about quadrants
    print "Number of quadrants: %d" % len(quads)
    
    for q in quads:
        print "  Quadrant %d" % q.quad()
        print "    virtual_channel: %s" % q.virtual_channel()
        print "    lane: %s" % q.lane()
        print "    tid: %s" % q.tid()
        print "    acq_count: %s" % q.acq_count()
        print "    op_code: %s" % q.op_code()
        print "    seq_count: %s" % q.seq_count()
        print "    ticks: %s" % q.ticks()
        print "    fiducials: %s" % q.fiducials()
        print "    frame_type: %s" % q.frame_type()
        print "    sb_temp: %s" % map(q.sb_temp, range(4))
            
        # image data as 3-dimentional array
        data = q.data()

So far so good. 'quads' is a list of CsPad Element objects, and not necessarily ordered in the expected way. So you'll need to use q.quad() to obtain the quad number.
q.data() gives you a 3D numpy array [row][col][sec]. Here sections will be ordered as expected, but be aware in case of missing sections, that you may need to check the
configuration object. You can get that from the env object, typically something you do in beginjob:

Code Block

	none
	none


def beginjob(self,evt,env):
    config = env.getConfig(xtc.TypeId.Type.Id_CspadConfig, self.img_source)
    if not config:
        print '*** cspad config object is missing ***'
        return        
    print "Cspad configuration"
    print "  N quadrants   : %d" % config.numQuads()
    print "  Quad mask     : %#x" % config.quadMask()
    print "  payloadSize   : %d" % config.payloadSize()
    print "  badAsicMask0  : %#x" % config.badAsicMask0()
    print "  badAsicMask1  : %#x" % config.badAsicMask1()
    print "  asicMask      : %#x" % config.asicMask()
    print "  numAsicsRead  : %d" % config.numAsicsRead()

   # get the indices of sections in use:
   qn = range(0,config.numQuads())               
   self.sections = map(config.sections, qn )

If you want to draw the whole CsPad image, there's currently no pyana function that does this. Pyana only supplies the pixels in a numpy array, and the
exact location of each pixel depends on the conditions at the time of data collection. A simplified way of making the image can be seen in cspad_simple.py(newer version (cspad.py) available if you check out the XtcExplorer package).

CSPad pixel coordinates.

The CSPad detector image can be drawn by positioning the sections from the data array into a large image array. This is done in cspad_simple.py above. The positions are extracted from optical meterology measurements and additional calibrations. Alternatively one can find the coordinate of each individual pixel from a pixel map, based on the same optical metrology measurements. This is described in details here

Epics Process Variables and ControlConfig

EPICS data is different from DAQ event data. It stores the conditions and settings of the instruments, but values typically change more slowly than your
average shot-by-shot data, and EPICS data is typically updated only when it changes, or every second, or similar. It is not stored in the 'evt' (event) object,
but in the 'env' (environment) object. You typically would read it only at the beginning of each job or if your doing a scan, you'd read it in every calibration cycle:

Code Block

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title	env.epicsStore()


def begincalibcycle(self,evt,env):

    ## The returned value should be of the type epics.EpicsPvTime.
    pv = env.epicsStore().value( pv_name )
    if not pv:
        logging.warning('EPICS PV %s does not exist', pv_name)
    else:
        value = pv.value 
        status = pv.status 
        alarm_severity = pv.severity

Code Block

	none
	none
title	ControlConfig


def begincalibcycle(self,evt,env):
    ctrl_config = env.getConfig(xtc.TypeId.Type.Id_ControlConfig)
    
    nControls = ctrl_config.npvControls()
    for ic in range (0, nControls ):

        cpv = ctrl_config.pvControl(ic)
        name = cpv.name()
        value = cpv.value()range(4)

    print 
    print "Cspad configuration"
    print "  N quadrants   : %d" % config.numQuads()
    print "  Quad mask     : %#x" % config.quadMask()
    print "  payloadSize   : %d" % config.payloadSize()
    print "  badAsicMask0  : %#x" % config.badAsicMask0()
    print "  badAsicMask1  : %#x" % config.badAsicMask1()
    print "  asicMask      : %#x" % config.asicMask()
    print "  numAsicsRead  : %d" % config.numAsicsRead()
    try:
        # older versions may not have all methods
        print "  roiMask       : [%s]" % ', '.join([hex(config.roiMask(q)) for q in quads])
        print "  numAsicsStored: %s" % str(map(config.numAsicsStored, quads))
    except:
        pass
    print "  sections      : %s" % str(map(config.sections, quads))
    print

Page tree

Versions Compared

Old Version 11

New Version Current

Key

Time data

IPIMB diode data

BeamLine Data: EBeam

BeamLine Data: FEE Gas Detector

BeamLine Data: Phase Cavity

Event code

Acqiris waveform data

Encoder data (delay scanner)

Time data

IPIMB diode data

Beamline data (Bld)

Display images from princeton camera

Acqiris waveform data

CsPad data

Princeton camera image

PnCCD image

Other image (FCCD*,Opal,PIM (TM6740), ... )

FCCD (Fast CCD) image

CsPad data

CSPad pixel coordinates.

Epics Process Variables and ControlConfig